ELEN6887 Lecture 14: Denoising Smooth Functions with Unknown Smoothness
نویسنده
چکیده
Lipschitz functions are interesting, but can be very rough (these can have many kinks). In many situations the functions can be much smoother. This is how you would model the temperature inside a museum room for example. Often we don’t know how smooth the function might be, so an interesting question is if we can adapt to the unknown smoothness. In this lecture we will use the Maximum Complexity-Regularized Likelihood Estimation result we derived in Lecture 14 to extend our denoising scheme in several important ways. To begin with let’s consider a broader class of functions.
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ELEN6887 Lecture 15: Denoising Smooth Functions with Unknown Smoothness
Lipschitz functions are interesting, but can be very rough (these can have many kinks). In many situations the functions can be much smoother. This is how you would model the temperature inside a museum room for example. Often we don’t know how smooth the function might be, so an interesting question is if we can adapt to the unknown smoothness. In this lecture we will use the Maximum Complexit...
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Lipschitz functions are interesting, but can be very rough (these can have many kinks). In many situations the functions can be much smoother. This is how you would model the temperature inside a museum room for example. Often we don’t know how smooth the function might be, so an interesting question is if we can adapt to the unknown smoothness. In this lecture we will use the Maximum Complexit...
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